Fear emotion is a typical negative emotion that is commonly present in daily life and significantly influences human behavior. A deeper understanding of the mechanisms underlying negative emotions contributes to the improvement of diagnosing and treating disorders related to negative emotions. However, the neural mechanisms of the brain when faced with fearful emotional stimuli remain unclear. To this end, this study further combined electroencephalogram (EEG) source analysis and cortical brain network construction based on early posterior negativity (EPN) analysis to explore the differences in brain information processing mechanisms under fearful and neutral emotional picture stimuli from a spatiotemporal perspective. The results revealed that neutral emotional stimuli could elicit higher EPN amplitudes compared to fearful stimuli. Further source analysis of EEG data containing EPN components revealed significant differences in brain cortical activation areas between fearful and neutral emotional stimuli. Subsequently, more functional connections were observed in the brain network in the alpha frequency band for fearful emotions compared to neutral emotions. By quantifying brain network properties, we found that the average node degree and average clustering coefficient under fearful emotional stimuli were significantly larger compared to neutral emotions. These results indicate that combining EPN analysis with EEG source component and brain network analysis helps to explore brain functional modulation in the processing of fearful emotions with higher spatiotemporal resolution, providing a new perspective on the neural mechanisms of negative emotions.
ObjectiveTo explore the effect of family-school-hospital application in continuous nursing care for children with epilepsy. Methods120 children with epilepsy admitted to Children's Hospital Affiliated to Jiangnan University from January 2021 to October 2022 were randomly divided into two groups, each with 60 cases. The control group received routine care, while the experimental group received family-school-hospital continuous care. Compare the awareness of epilepsy knowledge, disease control effectiveness, medication compliance, negative emotions, physical and mental status, and quality of life before and after nursing between the families of two groups of children with epilepsy. ResultsAfter 2 months of nursing care, the scores of family members' knowledge of epilepsy in the experimental group were higher than the control group (P<0.05). The effect of disease control in the experimental group was better the control group (P<0.05). The drug compliance of the experimental group was higher than the control group (P<0.05). The quality of life score in the intervention group was higher than the control group (P<0.05). ConclusionThe application of family-school-hospital in the continuous care of children with epilepsy can improve their family members' awareness of epilepsy knowledge, effectively control the disease, improve medication compliance, improve negative emotions and physical and mental conditions, and thus improve the quality of life of children.
Analyzing the influence of mixed emotional factors on false memory through brain function network is helpful to further explore the nature of brain memory. In this study, Deese-Roediger-Mc-Dermott (DRM) paradigm electroencephalogram (EEG) experiment was designed with mixed emotional memory materials, and different kinds of music were used to induce positive, calm and negative emotions of three groups of subjects. For the obtained false memory EEG signals, standardized low resolution brain electromagnetic tomography algorithm (sLORETA) was applied in the source localization, and then the functional network of cerebral cortex was built and analyzed. The results show that the positive group has the most false memories [(83.3 ± 6.8)%], the prefrontal lobe and left temporal lobe are activated, and the degree of activation and the density of brain network are significantly larger than those of the calm group and the negative group. In the calm group, the posterior prefrontal lobe and temporal lobe are activated, and the collectivization degree and the information transmission rate of brain network are larger than those of the positive and negative groups. The negative group has the least false memories [(73.3 ± 2.2)%], and the prefrontal lobe and right temporal lobe are activated. The brain network is the sparsest in the negative group, the degree of centralization is significantly larger than that of the calm group, but the collectivization degree and the information transmission rate of brain network are smaller than the positive group. The results show that the brain is stimulated by positive emotions, so more brain resources are used to memorize and associate words, which increases false memory. The activity of the brain is inhibited by negative emotions, which hinders the brain’s memory and association of words and reduces false memory.
Focused on the world-wide issue of improving the accuracy of emotion recognition, this paper proposes an electroencephalogram (EEG) signal feature extraction algorithm based on wavelet packet energy entropy and auto-regressive (AR) model. The auto-regressive process can be approached to EEG signal as much as possible, and provide a wealth of spectral information with few parameters. The wavelet packet entropy reflects the spectral energy distribution of the signal in each frequency band. Combination of them gives a better reflect of the energy characteristics of EEG signals. Feature extraction and fusion are implemented based on kernel principal component analysis. Six emotional states from a public multimodal database for emotion analysis using physiological signals (DEAP) are recognized. The results show that the recognition accuracy of the proposed algorithm is more than 90%, and the highest recognition accuracy is 99.33%. It indicates that this algorithm can extract the feature of EEG emotion well, and it is a kind of effective emotion feature extraction algorithm, providing support to emotion recognition.
ObjectiveTo examine the effect of preoperative adverse emotion on rehabilitation outcomes in lung cancer patients undergoing thoracoscopic major pulmonary resection.MethodsWe retrospectively analyzed the clinical data of 1 438 patients with lung cancer who underwent thoracoscopic lobectomy and segmentectomy in West China Hospital of Sichuan University from February 2017 to July 2018 including 555 males and 883 females. All patients were assessed by Huaxi emotional-distress index scoring, and were divided into three groups including a non-negative emotion group, a mild negative emotion group, and a moderate-severe negative emotion group. All patients underwent thoracoscopic lobectomy or segmentectomy plus systematic lymph node dissection or sampling. The volume of postoperative chest drainage, postoperative lung infection rate, time of chest tube intubation and postoperative duration of hospitalization were compared among these three groups.ResultsThere were different morbidities of adverse emotion in age, sex, education level and smoking among patients before operation (P<0.05). Univariate analysis showed that there was no statistical difference in the duration of indwelling drainage tube, drainage volume, postoperative pulmonary infection rate or the incidence of other complications among these three groups, but the duration of hospitalization in the latter two groups was less than that in the first group with a statistical difference (P<0.05). After correction of confounding factors by multiple regression analysis, there was no statistical difference among the three groups.ConclusionYoung patients are more likely to develop bad emotions, women are more likely to develop serious bad emotions, highly educated patients tend to develop bad emotions, and non-smoking patients tend to develop bad emotions. There is no effect of preoperative adverse emotions on the rapid recovery of lung cancer patients after minimally invasive thoracoscopic surgery.
Objective To analyze the clinical intervention effect of multi-disciplinary team (MDT) nursing mode on patients after transcatheter aortic valve implantation (TAVI). Methods A total of 89 patients who were admitted to our hospital and underwent TAVI surgery from April to December 2021 were selected, including 64 males and 25 females, with an average age of 64.7±11.8 years. The subjects were divided into a MDT intervention group (n=42) and a control group (n=47) according to different postoperative nursing intervention methods. Clinical effectivenesses were compared between the two groups. Results The left ventricular ejection fraction in the two groups significantly increased on the 7th day after the operation, and the increase in the MDT intervention group was more obvious, with no statistical difference between the two groups (P=0.14). On the 7th day after surgery, forced vital capacity/predicated value and forced expiratory volume in one second/predicated value significantly decreased, and decreased more significantly in the control group than those in the MDT intervention group with statistical differences (P=0.01). The ICU stay time (P=0.01), hospital stay time (P<0.01) and total postoperative pulmonary complications rate (P=0.03) in the MDT intervention group were significantly shorter or lower than those in the control group The evaluation results of the anxiety and depression status of the patients before and after nursing intervention showed that the scores of anxiety and depression in the two groups were significantly lower than before, and the scores of each scale in the MDT intervention group were lower. The score of quality of life of the two groups significantly improved at the end of 6 months after surgery, and in the MDT intervention group it was significantly higher than that in the control group (P=0.02). Conclusion MDT intervention mode can promote the rapid recovery of patients after TAVI, effectively reduce the risk of postoperative pulmonary complications, and improve the postoperative quality of life.
Emotion plays an important role in people's cognition and communication. By analyzing electroencephalogram (EEG) signals to identify internal emotions and feedback emotional information in an active or passive way, affective brain-computer interactions can effectively promote human-computer interaction. This paper focuses on emotion recognition using EEG. We systematically evaluate the performance of state-of-the-art feature extraction and classification methods with a public-available dataset for emotion analysis using physiological signals (DEAP). The common random split method will lead to high correlation between training and testing samples. Thus, we use block-wise K fold cross validation. Moreover, we compare the accuracy of emotion recognition with different time window length. The experimental results indicate that 4 s time window is appropriate for sampling. Filter-bank long short-term memory networks (FBLSTM) using differential entropy features as input was proposed. The average accuracy of low and high in valance dimension, arousal dimension and combination of the four in valance-arousal plane is 78.8%, 78.4% and 70.3%, respectively. These results demonstrate the advantage of our emotion recognition model over the current studies in terms of classification accuracy. Our model might provide a novel method for emotion recognition in affective brain-computer interactions.
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
In order to monitor the emotional state changes of audience on real-time and to adjust the music playlist, we proposed an algorithm framework of an electroencephalogram (EEG) driven personalized affective music recommendation system based on the portable dry electrode shown in this paper. We also further finished a preliminary implementation on the Android platform. We used a two-dimensional emotional model of arousal and valence as the reference, and mapped the EEG data and the corresponding seed songs to the emotional coordinate quadrant in order to establish the matching relationship. Then, Mel frequency cepstrum coefficients were applied to evaluate the similarity between the seed songs and the songs in music library. In the end, during the music playing state, we used the EEG data to identify the audience’s emotional state, and played and adjusted the corresponding song playlist based on the established matching relationship.
Childhood is the key period of psychological and behavioral development of children. The changes of children's psychological behavior during this period have an impact on the psychological and behavioral patterns of adolescents and even adults. Epilepsy is a chronic and recurrent disease, which affect the development emotional behavior problem of children with epilepsy seriously. This paper reviewed the influencing factors, measuring methods and intervention of emotional behavior problems in children with epilepsy so as to alleviate the negative emotion and behavior problems and provide quality of life in children with epilepsy.